Browse categories
Explore
Fiverr Pro
English
$
USD
I will build custom python etl pipelines and apis
Senior Data Engineer and Automated ETL Cloud Systems Specialist
I am a Data Engineer & Automation Specialist with 3+ years of experience designing robust data architectures. I eliminate manual engineering bottlenecks by building self-healing ETL pipelines and seam...
About this Gig
Are broken APIs and manual data processing slowing down your workflows?
Modern architectures require data synchronization that is automated, scalable, and completely hands-off. As an experienced Data Engineer, I specialize in building custom Python ETL pipelines and robust API integrations designed to handle your technical operations smoothly.
Whether you need to stream live third-party metrics, manage massive datasets, or clean complex data structures, I deliver secure, production-grade automated solutions.
️ My Core Technical Services:
* Custom API Integrations: Seamless extraction and sync from any REST endpoint or webhook.
* Automated ETL Pipelines: Highly efficient Extract, Transform, and Load scripts using Python.
* Data Validation & Cleaning: Strict data structuring to eliminate processing errors before it hits your target location.
* Database Synchronization: Automated pushing and matching of schemas into SQL or NoSQL databases.
* Automated Cron Scheduling: Setting up self-healing triggers so your pipelines run flawlessly without manual intervention.
Let's build a fast, secure, and production-ready data system. Contact me before ordering to align on your tech stack!
FAQ
Do I need to provide API documentation?
Yes, providing the API documentation or endpoints from your service provider helps speed up the development process. However, if you don't have it, just let me know the tools you use, and I can research the integration paths for you.
Where will the automated script actually run?
Depending on your current setup, it can run on your local machine, a local Linux environment, or be deployed to cloud virtual servers. I will ensure the runtime environment matches your operational baseline perfectly.
How do you ensure the data doesn't corrupt during the pipeline run?
I build strict data quality and validation checks (using Pydantic or schema constraints) into the ingestion phase. If a payload doesn't match the required format, the pipeline logs it and triggers an alert rather than breaking your database.
